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Emerging Trends in Knowledge Discovery and Data Mining

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Cover of 'Emerging Trends in Knowledge Discovery and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Modality Classification for Medical Images Using Sparse Coded Affine-Invariant Descriptors
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    Chapter 2 Mining Web Data for Epidemiological Surveillance
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    Chapter 3 Getting a Grasp on Clinical Pathway Data: An Approach Based on Process Mining
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    Chapter 4 ALIVE: A Multi-relational Link Prediction Environment for the Healthcare Domain
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    Chapter 5 The Relevance of Spatial Relation Terms and Geographical Feature Types
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    Chapter 6 Applying NLP Techniques for Query Reformulation to Information Retrieval with Geographical References
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    Chapter 7 Adaptive Evidence Accumulation Clustering Using the Confidence of the Objects’ Assignments
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    Chapter 8 An Explicit Description of the Extended Gaussian Kernel
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    Chapter 9 An Improved Genetic Clustering Algorithm for Categorical Data
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    Chapter 10 Instance-Ranking: A New Perspective to Consider the Instance Dependency for Classification
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    Chapter 11 Triangular Kernel Nearest-Neighbor-Based Clustering Algorithm for Discovering True Clusters
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    Chapter 12 DisClose : Dis covering Colossal Close d Itemsets via a Memory Efficient Compact Row-Tree
Attention for Chapter 12: DisClose : Dis covering Colossal Close d Itemsets via a Memory Efficient Compact Row-Tree
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Chapter title
DisClose : Dis covering Colossal Close d Itemsets via a Memory Efficient Compact Row-Tree
Chapter number 12
Book title
Emerging Trends in Knowledge Discovery and Data Mining
Published by
Springer, Berlin, Heidelberg, May 2012
DOI 10.1007/978-3-642-36778-6_12
Book ISBNs
978-3-64-236777-9, 978-3-64-236778-6
Authors

Nurul F. Zulkurnain, David J. Haglin, John A. Keane

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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